Health Informatics & Data Science | Machine Learning in Healthcare | Innovation Enthusiast

Passionate about using data, AI, and health tech to transform patient care and empower providers.
Experience in predictive modeling, clinical analytics, and digital health innovation.

I am a graduate student in Health Informatics & Data Science at Georgetown University, with a strong focus on applying machine learning and advanced analytics to healthcare problems.

My work spans clinical decision support, predictive modeling, health data interoperability, and blockchain applications in healthcare. I enjoy breaking down complex problems into actionable solutions that improve patient outcomes, provider workflows, and system efficiency.

• Healthcare Data Science – EHR data analysis, patient outcomes prediction, survival analysis, and quality improvement analytics
• Machine Learning – Predictive modeling, natural language processing (NLP), random forests, logistic regression, neural networks
• Data Visualization – Tableau, Looker Studio, Matplotlib, Seaborn for clear and impactful storytelling with health data
• Health Tech & Innovation – FHIR-based interoperability, blockchain in healthcare, remote patient monitoring, digital health solutions

Developed ML models to predict toxicities in melanoma patients receiving immune checkpoint inhibitor (ICI) therapy.
Tools: Python (scikit-learn), Pandas, NumPy, Matplotlib

• Engineered clinical features, comorbidity indices, and drug categories
• Applied logistic regression and random forest models to structured EHR data

Built deep learning models to classify severity of lumbar spine degenerative conditions from MRI scans.
Tools: Python (PyTorch), NumPy, Pandas, Matplotlib

• Preprocessed sagittal and axial T2-weighted MRI slices into structured datasets
• Developed CNN models for multi-class classification of stenosis severity
• Evaluated performance using accuracy, AUROC, and confusion matrices

Conceptualized a SwiftUI iOS rehabilitation tracking app for post-operative hand therapy.
Tools: Swift, AWS, FHIR APIs, Figma, Survey Design

• Conducted patient & provider need assessments and created personas
• Designed workflows (swimlanes), app architecture, and AWS-based tech stack
• Developed HIPAA-compliant security plan and EHR interoperability via FHIR
• Ran usability testing, collected feedback, and built implementation roadmap

• Data Science & Analytics: Python, R, SQL, Pandas, Scikit-learn, TensorFlow
• Machine Learning: Predictive analytics, NLP, classification/regression models, ensemble learning
• Healthcare Data: EHR data analysis, FHIR APIs, HL7, OMOP CDM, population health analytics
• Visualization Tools: Tableau, Looker Studio, Matplotlib
• Cloud & Platforms: AWS, Google Cloud, GitHub, PostgreSQL

• LinkedIn: www.linkedin.com/in/austin-cherian2023
• Portfolio: github.com/austin3393
• Certifications Google Data Analytics Professional Certification
• Email: austin.cherian17@gmail.com